Diagnosis analysis

workflowr

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Last updated: 2023-06-24

Checks: 7 0

Knit directory: scRNA_Qualitycheck/

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---

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File	Version	Author	Date	Message
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---

recalc=T
if(recalc){
  load(file = "./output/scRNA_seurat_integrated.Rdata")}
if(!recalc){
  load(file = "./output/scRNA_seurat_integrated.Rdata")
}

Identifying markers LMER

Model genexpression ~ Diagnosis+(1|TR)+(1|BatchID)

  coldata = seurat_integrated@meta.data
  countdata = as.matrix(seurat_integrated@assays$SCT@scale.data)
  coldata$TR = gsub(".*(TR.)", "\\1", coldata$SampleID)
  
  model = as.formula("x ~ Diagnosis+(1|TR)+(1|BatchID)")
  
if(recalc){  
  runlmer = function(i, mod, gdata, cdata){
    #cat(i, end="\r")
    cdata$x=gdata[i,]
    res = suppressMessages(lmer(mod, data=cdata, verbose = 0))
    res = summary(res)
    return(res$coefficients[2,])
  }
  
  reslmer <- lapply(1:nrow(countdata), function(i){
    runlmer(i, gdata = countdata, mod = model, cdata = coldata)})
  names(reslmer) = rownames(countdata)
  reslmer <- do.call(rbind, reslmer)
  reslmer <- as.data.frame(reslmer)
  reslmer$p.value = 2*pt(abs(reslmer$`t value`), df=ncol(countdata)-1, lower=FALSE)
  reslmer$adj.p = p.adjust(reslmer$p, method="BH")
}

ggplot(reslmer, aes(x=Estimate, y=-log10(p.value), col=(adj.p<0.05)))+
  geom_point()

Plot markers LMER

Identifying markers for Diagnosis

if(recalc){
  Idents(seurat_integrated) = "Diagnosis"
  
  Diagnosis.Markers= FindMarkers(seurat_integrated, 
                                assay = "integrated",
                                test.use = "wilcox",
                                ident.1 = "Ctrl", only.pos = F)
  
  Diagnosis.Markers <- Diagnosis.Markers[Diagnosis.Markers$p_val_adj<0.05,]
  Diagnosis.Markers %>%
    top_n(n = 20, wt = abs(avg_log2FC)) -> top10Diagnosis.Markers
  
  Diagnosis.Markers = Diagnosis.Markers[order(Diagnosis.Markers$avg_log2FC),]
}

DoHeatmap(seurat_integrated, features = rownames(top10Diagnosis.Markers), 
          group.by = "Diagnosis", assay = "integrated")

Heatmap grouped by Diagnosis

Calculating cell percentages

#Calculating the percentages of cell per cluster
number_perCluster<- as.data.frame.matrix(table(Samples=seurat_integrated$SampleID, 
                                               Clusters=scRNA.sce$seurat_clusters))
prop_per_Sample<- prop.table(table(Samples=scRNA.sce$Sample, 
                                   Clusters=scRNA.sce$seurat_clusters), margin = 1)

SampleData = unique(coldata[,c("SampleID", "BatchID", "Diagnosis", "TR")])


res = apply(prop_per_Sample, 2, function(x){summary(lmer(x~Diagnosis+(1|TR), data=SampleData))})

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

ResFreq <- lapply(res, function(x) x$coefficients[2,]) %>% do.call(rbind,.) %>% as.data.frame()

ResFreq$pvalue = 2*pt(abs(ResFreq$`t value`), df=10, lower.tail = F)
ResFreq$p.adj = p.adjust(ResFreq$pvalue)

DT::datatable(ResFreq, extensions = "Buttons",
              filter="top",
              caption = paste0("Effects of Diagnosis on cluster"),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              ))

Genewise based cell cluster

Model

only within clusters genexpression ~ Diagnosis+(1|TR)+(1|BatchID)

if(recalc){
  reslist.by.cluster=list()
  for(i in unique(seurat_integrated$seurat_clusters)){
    #print(i)
    cellidx=coldata$seurat_clusters==i
    countdata.subset=countdata[,cellidx]
    reslmer.cluster <- lapply(1:nrow(countdata), function(i){
      runlmer(i, gdata = countdata.subset, mod = model, cdata = coldata[cellidx,])})
    reslmer.cluster <- do.call(rbind, reslmer.cluster)
    reslmer.cluster <- as.data.frame(reslmer.cluster)
    reslmer.cluster$p.value = 2*pt(abs(reslmer.cluster$`t value`), df=ncol(countdata)-1, lower=FALSE)
    reslmer.cluster$adj.p = p.adjust(reslmer.cluster$p, method="BH")
    row.names(reslmer.cluster) = rownames(countdata)
    reslist.by.cluster[[i]] = reslmer.cluster
    
  }

  
names(reslist.by.cluster) <- unique(seurat_integrated$seurat_clusters)

reslist.by.cluster = reslist.by.cluster[as.character(sort(unique(seurat_integrated$seurat_clusters)))]

DEX.reslist.by.cluster = lapply(reslist.by.cluster, function(x){rownames(x)[x$adj.p<=0.05 & 
                                                                              x$Estimate>=0.2]})

names(DEX.reslist.by.cluster)  <- names(reslist.by.cluster) 

gbackground = rownames(seurat_integrated)

Gost.reslist.by.cluster = lapply(DEX.reslist.by.cluster, function(x){gost(x, custom_bg = gbackground,
                                                                          evcodes = T)})

names(Gost.reslist.by.cluster)  <- names(reslist.by.cluster) 

}

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'
Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'
Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Detected custom background input, domain scope is set to 'custom'

No results to show
Please make sure that the organism is correct or set significant = FALSE

Results Cluster 0

x="0"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 1

x="1"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

Results Cluster 2

x="2"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 3

x="3"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 4

x="4"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 5

x="5"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

Results Cluster 6

x="6"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 7

x="7"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 8

x="8"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 9

x="9"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 10

x="10"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 11

x="11"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 12

x="12"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 13

x="13"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

## Results Cluster 14

x="14"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

if(is.null(Gost.reslist.by.cluster[[x]])){
  ggplot() +                      # Draw ggplot2 plot with text only
  annotate("text",
           x = 1,
           y = 1,
           size = 8,
           label = "no significant GO-terms associated") + 
  theme_void()
} else {
gostplot(Gost.reslist.by.cluster[[x]])}

save(reslist.by.cluster, DEX.reslist.by.cluster, top10Diagnosis.Markers, Gost.reslist.by.cluster, ResFreq, Diagnosis.Markers, reslmer, seurat_integrated, scRNA.sce, Cellpalette, SampleGPalette, SamplePalette, prop_per_Sample, file = "./output/scRNA_seurat_integrated.Rdata")

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] lme4_1.1-31                 Matrix_1.5-3               
 [3] gprofiler2_0.2.1            viridis_0.6.2              
 [5] viridisLite_0.4.1           cowplot_1.1.1              
 [7] randomcoloR_1.1.0.1         RCurl_1.98-1.10            
 [9] RColorBrewer_1.1-3          data.table_1.14.6          
[11] lubridate_1.9.1             forcats_1.0.0              
[13] stringr_1.5.0               dplyr_1.1.0                
[15] purrr_1.0.1                 readr_2.1.3                
[17] tidyr_1.3.0                 tibble_3.1.8               
[19] tidyverse_1.3.2             scater_1.26.1              
[21] org.Hs.eg.db_3.16.0         AnnotationDbi_1.60.0       
[23] AnnotationHub_3.6.0         BiocFileCache_2.6.0        
[25] dbplyr_2.3.0                celldex_1.8.0              
[27] scuttle_1.8.4               ggplot2_3.4.0              
[29] slingshot_2.6.0             TrajectoryUtils_1.6.0      
[31] princurve_2.1.6             SeuratObject_4.1.3         
[33] Seurat_4.3.0                DropletUtils_1.18.1        
[35] SingleCellExperiment_1.20.0 SingleR_2.0.0              
[37] SummarizedExperiment_1.28.0 Biobase_2.58.0             
[39] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
[41] IRanges_2.32.0              S4Vectors_0.36.1           
[43] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
[45] matrixStats_0.63.0          workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3                scattermore_0.8              
  [3] R.methodsS3_1.8.2             bit64_4.0.5                  
  [5] knitr_1.42                    irlba_2.3.5.1                
  [7] DelayedArray_0.24.0           R.utils_2.12.2               
  [9] KEGGREST_1.38.0               generics_0.1.3               
 [11] ScaledMatrix_1.6.0            callr_3.7.3                  
 [13] RSQLite_2.2.20                RANN_2.6.1                   
 [15] future_1.31.0                 tzdb_0.3.0                   
 [17] bit_4.0.5                     xml2_1.3.3                   
 [19] spatstat.data_3.0-0           httpuv_1.6.8                 
 [21] assertthat_0.2.1              gargle_1.3.0                 
 [23] xfun_0.37                     hms_1.1.2                    
 [25] jquerylib_0.1.4               evaluate_0.20                
 [27] promises_1.2.0.1              fansi_1.0.4                  
 [29] readxl_1.4.1                  igraph_1.3.5                 
 [31] DBI_1.1.3                     htmlwidgets_1.6.1            
 [33] spatstat.geom_3.0-6           googledrive_2.0.0            
 [35] ellipsis_0.3.2                crosstalk_1.2.0              
 [37] backports_1.4.1               V8_4.2.2                     
 [39] deldir_1.0-6                  sparseMatrixStats_1.10.0     
 [41] vctrs_0.5.2                   ROCR_1.0-11                  
 [43] abind_1.4-5                   cachem_1.0.6                 
 [45] withr_2.5.0                   progressr_0.13.0             
 [47] sctransform_0.3.5             goftest_1.2-3                
 [49] cluster_2.1.4                 ExperimentHub_2.6.0          
 [51] lazyeval_0.2.2                crayon_1.5.2                 
 [53] spatstat.explore_3.0-6        labeling_0.4.2               
 [55] edgeR_3.40.2                  pkgconfig_2.0.3              
 [57] nlme_3.1-162                  vipor_0.4.5                  
 [59] rlang_1.0.6                   globals_0.16.2               
 [61] lifecycle_1.0.3               miniUI_0.1.1.1               
 [63] filelock_1.0.2                modelr_0.1.10                
 [65] rsvd_1.0.5                    cellranger_1.1.0             
 [67] rprojroot_2.0.3               polyclip_1.10-4              
 [69] lmtest_0.9-40                 boot_1.3-28.1                
 [71] Rhdf5lib_1.20.0               zoo_1.8-11                   
 [73] reprex_2.0.2                  beeswarm_0.4.0               
 [75] googlesheets4_1.0.1           whisker_0.4.1                
 [77] ggridges_0.5.4                processx_3.8.0               
 [79] png_0.1-8                     bitops_1.0-7                 
 [81] getPass_0.2-2                 R.oo_1.25.0                  
 [83] KernSmooth_2.23-20            rhdf5filters_1.10.0          
 [85] Biostrings_2.66.0             blob_1.2.3                   
 [87] DelayedMatrixStats_1.20.0     parallelly_1.34.0            
 [89] spatstat.random_3.1-3         beachmat_2.14.0              
 [91] scales_1.2.1                  memoise_2.0.1                
 [93] magrittr_2.0.3                plyr_1.8.8                   
 [95] ica_1.0-3                     zlibbioc_1.44.0              
 [97] compiler_4.2.2                dqrng_0.3.0                  
 [99] fitdistrplus_1.1-8            cli_3.6.0                    
[101] XVector_0.38.0                listenv_0.9.0                
[103] patchwork_1.1.2               pbapply_1.7-0                
[105] ps_1.7.2                      MASS_7.3-58.2                
[107] tidyselect_1.2.0              stringi_1.7.12               
[109] highr_0.10                    yaml_2.3.7                   
[111] BiocSingular_1.14.0           locfit_1.5-9.7               
[113] ggrepel_0.9.3                 grid_4.2.2                   
[115] sass_0.4.5                    timechange_0.2.0             
[117] tools_4.2.2                   future.apply_1.10.0          
[119] parallel_4.2.2                rstudioapi_0.14              
[121] git2r_0.31.0                  gridExtra_2.3                
[123] farver_2.1.1                  Rtsne_0.16                   
[125] digest_0.6.31                 BiocManager_1.30.19          
[127] shiny_1.7.4                   Rcpp_1.0.10                  
[129] broom_1.0.3                   BiocVersion_3.16.0           
[131] later_1.3.0                   RcppAnnoy_0.0.20             
[133] httr_1.4.4                    colorspace_2.1-0             
[135] rvest_1.0.3                   fs_1.6.1                     
[137] tensor_1.5                    reticulate_1.28              
[139] splines_4.2.2                 uwot_0.1.14                  
[141] spatstat.utils_3.0-1          sp_1.6-0                     
[143] plotly_4.10.1                 xtable_1.8-4                 
[145] nloptr_2.0.3                  jsonlite_1.8.4               
[147] R6_2.5.1                      pillar_1.8.1                 
[149] htmltools_0.5.4               mime_0.12                    
[151] DT_0.27                       minqa_1.2.5                  
[153] glue_1.6.2                    fastmap_1.1.0                
[155] BiocParallel_1.32.5           BiocNeighbors_1.16.0         
[157] interactiveDisplayBase_1.36.0 codetools_0.2-19             
[159] utf8_1.2.3                    lattice_0.20-45              
[161] bslib_0.4.2                   spatstat.sparse_3.0-0        
[163] curl_5.0.0                    ggbeeswarm_0.7.1             
[165] leiden_0.4.3                  survival_3.5-0               
[167] limma_3.54.1                  rmarkdown_2.20               
[169] munsell_0.5.0                 rhdf5_2.42.0                 
[171] GenomeInfoDbData_1.2.9        HDF5Array_1.26.0             
[173] haven_2.5.1                   reshape2_1.4.4               
[175] gtable_0.3.1